Merging samples

Nascimento + Franjic // SoupX/DoubletFinder/DropletQC Pipeline

# Loading necessary libraries
library(tidyverse)
library(Seurat)
library(viridis)
library(patchwork)
library(DropletUtils)
library(future)
library(tibble)
library(ComplexHeatmap)
library(GEOquery)

# Additional color scales
library(MetBrewer)
library(NatParksPalettes)

# Parallel processing setup
plan("multicore", workers = 80)
options(future.globals.maxSize = 30 * 1024^3) # 30 GB

# Custom themes and scales for plots
mytheme <- theme_minimal() + 
  theme(axis.line = element_line(),
        axis.ticks = element_line(),
        text = element_text(family = "Helvetica"))

simple <- NoAxes() + NoLegend()
mysc <- scale_color_viridis(option = "A")
region.pal <- c("#5EBFA2", "#F69663", "#731DD8",  "#FB7C7E")

# List of sex-specific genes
sex.genes <- c("TTTY14", "NLGN4Y", "USP9Y", "UTY", "XIST", "RPS4X", "TMSB4X", "TSIX")

#levels for some metadata categories:
age.levels <- c("23GW", "14d", "33d", "54d", "2y", "3y", "13y", "27y", "50y", "51y", "79y")
age.group.levels <- c("Fetal (23GW)", "Infant (14d-54d)", "Toddler (2y-3y)", "Teen (13y)", "Adult (27y-79y)")
region.levels <- c("Germinal Zone", "Embryonic EC", "Migratory Stream", "Postnatal EC")

#Creating seurat objects from count matrices Count matrices and metadata are downloaded from GEO and saved in a folder named “matrices”.

download.file("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE199762&format=file&file=GSE199762%5Fsamples%5Ffrom%5FGSE186538%2Etar%2Egz", 
              method = "curl", 
              "matrices/franjic_et_al_samples.tar.gz")

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 83  709M   83  594M    0     0  1652k      0  0:07:19  0:06:08  0:01:11 1906k
 84  709M   84  596M    0     0  1653k      0  0:07:19  0:06:09  0:01:10 1954k
 84  709M   84  598M    0     0  1654k      0  0:07:18  0:06:10  0:01:08 1938k
 84  709M   84  599M    0     0  1653k      0  0:07:19  0:06:11  0:01:08 1786k
 84  709M   84  601M    0     0  1652k      0  0:07:19  0:06:12  0:01:07 1720k
 84  709M   84  602M    0     0  1652k      0  0:07:19  0:06:13  0:01:06 1645k
 85  709M   85  604M    0     0  1652k      0  0:07:19  0:06:14  0:01:05 1580k
 85  709M   85  606M    0     0  1653k      0  0:07:19  0:06:15  0:01:04 1585k
 85  709M   85  608M    0     0  1654k      0  0:07:18  0:06:16  0:01:02 1765k
 86  709M   86  610M    0     0  1656k      0  0:07:18  0:06:17  0:01:01 1949k
 86  709M   86  613M    0     0  1658k      0  0:07:17  0:06:18  0:00:59 2118k
 86  709M   86  615M    0     0  1661k      0  0:07:17  0:06:19  0:00:58 2333k
 87  709M   87  619M    0     0  1667k      0  0:07:15  0:06:20  0:00:55 2701k
 88  709M   88  624M    0     0  1676k      0  0:07:13  0:06:21  0:00:52 3276k
 88  709M   88  630M    0     0  1686k      0  0:07:10  0:06:22  0:00:48 4002k
 89  709M   89  635M    0     0  1696k      0  0:07:08  0:06:23  0:00:45 4593k
 90  709M   90  638M    0     0  1700k      0  0:07:06  0:06:24  0:00:42 4700k
 90  709M   90  642M    0     0  1705k      0  0:07:05  0:06:25  0:00:40 4636k
 91  709M   91  645M    0     0  1710k      0  0:07:04  0:06:26  0:00:38 4332k
 91  709M   91  649M    0     0  1715k      0  0:07:03  0:06:27  0:00:36 3889k
 92  709M   92  652M    0     0  1720k      0  0:07:02  0:06:28  0:00:34 3545k
 92  709M   92  656M    0     0  1726k      0  0:07:00  0:06:29  0:00:31 3669k
 93  709M   93  659M    0     0  1730k      0  0:06:59  0:06:30  0:00:29 3661k
 93  709M   93  662M    0     0  1731k      0  0:06:59  0:06:31  0:00:28 3363k
 93  709M   93  663M    0     0  1730k      0  0:06:59  0:06:32  0:00:27 2892k
 93  709M   93  664M    0     0  1729k      0  0:06:59  0:06:33  0:00:26 2429k
 93  709M   93  665M    0     0  1728k      0  0:07:00  0:06:34  0:00:26 1881k
 94  709M   94  667M    0     0  1727k      0  0:07:00  0:06:35  0:00:25 1495k
 94  709M   94  668M    0     0  1727k      0  0:07:00  0:06:36  0:00:24 1386k
 94  709M   94  670M    0     0  1726k      0  0:07:00  0:06:37  0:00:23 1412k
 94  709M   94  671M    0     0  1725k      0  0:07:00  0:06:38  0:00:22 1440k
 94  709M   94  673M    0     0  1725k      0  0:07:00  0:06:39  0:00:21 1523k
 95  709M   95  674M    0     0  1724k      0  0:07:00  0:06:40  0:00:20 1526k
 95  709M   95  676M    0     0  1724k      0  0:07:01  0:06:41  0:00:20 1516k
 95  709M   95  677M    0     0  1724k      0  0:07:01  0:06:42  0:00:19 1556k
 95  709M   95  679M    0     0  1724k      0  0:07:01  0:06:43  0:00:18 1602k
 96  709M   96  681M    0     0  1724k      0  0:07:01  0:06:44  0:00:17 1660k
 96  709M   96  683M    0     0  1725k      0  0:07:00  0:06:45  0:00:15 1788k
 96  709M   96  685M    0     0  1727k      0  0:07:00  0:06:46  0:00:14 1938k
 96  709M   96  687M    0     0  1727k      0  0:07:00  0:06:47  0:00:13 1988k
 97  709M   97  689M    0     0  1728k      0  0:07:00  0:06:48  0:00:12 2068k
 97  709M   97  691M    0     0  1729k      0  0:06:59  0:06:49  0:00:10 2108k
 97  709M   97  693M    0     0  1729k      0  0:06:59  0:06:50  0:00:09 2058k
 98  709M   98  695M    0     0  1730k      0  0:06:59  0:06:51  0:00:08 1989k
 98  709M   98  697M    0     0  1731k      0  0:06:59  0:06:52  0:00:07 2045k
 98  709M   98  699M    0     0  1732k      0  0:06:59  0:06:53  0:00:06 2080k
 99  709M   99  702M    0     0  1734k      0  0:06:58  0:06:54  0:00:04 2136k
 99  709M   99  704M    0     0  1736k      0  0:06:58  0:06:55  0:00:03 2295k
 99  709M   99  707M    0     0  1738k      0  0:06:57  0:06:56  0:00:01 2437k
 99  709M   99  709M    0     0  1738k      0  0:06:57  0:06:57 --:--:-- 2364k
100  709M  100  709M    0     0  1738k      0  0:06:57  0:06:57 --:--:-- 2381k

srt_objects <- list()

for (s in c(gsm_samples$samplenames, "hsb231", "hsb237", "hsb628")) {
  cat(paste0("Importing sample ", s, "\n"))
  matrix <- ReadMtx(mtx = paste0("matrices/", s, "_counts.mtx"), 
                    features = paste0("matrices/", s, "_genes.tsv"), feature.column = 1, 
                    cells = paste0("matrices/", s, "_barcodes.tsv"))
  metadata <- read.csv(paste0("matrices/", s, "_metadata.csv"), row.names = 1)
  srt_objects[[s]] <- CreateSeuratObject(counts = matrix, meta.data = metadata)
}

Importing sample CGE
Importing sample MGE
Importing sample LGE
Importing sample EC_Stream
Importing sample dEC
Importing sample H71
Importing sample H31
Importing sample H37
Importing sample H48-g1
Importing sample H48-g2
Importing sample H39-g1
Importing sample H39-g2
Importing sample H46-g1
Importing sample H46-g2
Importing sample H29-g1
Importing sample H29-g2
Importing sample H33-g1
Importing sample H33-g2
Importing sample hsb231
Importing sample hsb237
Importing sample hsb628

Merging samples in a single Seurat object

all.exp = merge(srt_objects[[1]], srt_objects[-1])

all.exp@meta.data$region = factor(all.exp@meta.data$region, region.levels)
all.exp@meta.data$age = factor(all.exp@meta.data$age, levels = age.levels)

log Normalization

all.exp@active.assay = "RNA"
all.exp = all.exp %>% 
              NormalizeData(assay = "RNA", 
                            verbose = F) %>% 
              FindVariableFeatures() %>% 
              ScaleData()

Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

  
VariableFeatures(all.exp@assays$RNA) = all.exp@assays$RNA@var.features[!(all.exp@assays$RNA@var.features %in% sex.genes)]


all.exp = all.exp %>% RunPCA(npcs = 50)

PC_ 1 
Positive:  NEAT1, B2M, IGFBP7, SLC1A3, CLDN5, ATP1A2, FLT1, MT2A, EPAS1, HLA-E 
       MECOM, VIM, LEF1, IFITM3, APOE, MYO10, CLEC3B, ID1, ABCG2, PREX2 
       ITM2A, FLI1, CST3, IFI27, ITPR2, FN1, ID3, MTUS1, CGNL1, ITIH5 
Negative:  RALYL, RYR2, KCNQ5, STXBP5L, KHDRBS2, LINGO2, FRMPD4, PTPRR, MIR137HG, GRM7 
       CNTNAP5, KCNB2, SNAP25, ZNF385B, HCN1, CHRM3, ASIC2, SV2B, CDH18, OLFM3 
       AL008633.1, CDH12, MLIP, ST6GALNAC5, GRM1, CACNG3, LINC01250, CNTN5, EPHA6, CHSY3 
PC_ 2 
Positive:  FLT1, CLDN5, COBLL1, MECOM, LEF1, ABCB1, ARHGAP29, ADGRF5, ABCG2, EGFL7 
       PODXL, ERG, EPAS1, SLC7A5, EBF1, JCAD, CLEC3B, PTPRB, ITIH5, PRKCH 
       PECAM1, IFI27, ITM2A, KLF2, ITGA1, VWF, LINC02147, ST8SIA6, FN1, TBX3 
Negative:  ERBB4, SOX2-OT, ST18, PLP1, TMEM144, UGT8, ZNF536, TF, SOX6, DOCK10 
       MBP, AL589740.1, MOBP, LINC00609, BCAS1, DOCK5, CRYAB, MOG, CNP, ANLN 
       CERCAM, CNDP1, ENPP2, LPAR1, SCD, LINC01608, NKX6-2, PLEKHH1, FA2H, CLDN11 
PC_ 3 
Positive:  ADGRV1, GLIS3, CFAP47, CFAP54, CFAP157, ID4, AQP4, DTHD1, ADGB, BMPR1B 
       AC104078.2, CFAP73, CFAP299, WDR49, TCTEX1D1, CFAP43, IQGAP2, DNAAF1, CFAP52, ARMC3 
       DCDC1, VWA3A, SPATA17, CCDC173, SPAG17, GFAP, STK33, LRRIQ1, LRRC9, TTC29 
Negative:  MBP, PLP1, ST18, MOBP, RNF220, SLC24A2, TF, ENPP2, TMEM144, UGT8 
       MOG, CNP, CLDN11, DOCK10, SPOCK3, AC012494.1, DBNDD2, ERMN, PLEKHH1, NKX6-2 
       DOCK5, C10orf90, LINC01608, MAG, FRMD4B, CNDP1, CDK18, ANLN, SLCO1A2, OPALIN 
PC_ 4 
Positive:  GAD1, DLX6-AS1, NXPH1, GAD2, GRIK1, LHFPL3, ADARB2, ZNF385D, VWC2, ERBB4 
       IGF1, AC068308.1, SOX4, AC125613.1, MAF, SOX11, ST8SIA4, KIF26B, PTCHD4, KCNC2 
       SLC6A1-AS1, ELAVL2, FSTL5, ZNF804A, AC117461.1, SLC6A1, EGFR, SOX6, SDK1, PTPRM 
Negative:  CFAP157, DNAAF1, CFAP299, DTHD1, ADGB, CFAP73, AC104078.2, DNAH12, PLP1, CFAP52 
       ST18, MOBP, RNF220, CFAP43, TCTEX1D1, TTC29, ENPP2, TMEM144, ARMC3, TF 
       CAPS, SLC47A2, ROPN1L, PEX5L, RSPH1, MBP, WDR63, LINC02416, SPAG8, MOG 
PC_ 5 
Positive:  ERBB4, GAD1, SLC6A1, NXPH1, ADARB2, SOX2-OT, GAD2, DLX6-AS1, SLC6A1-AS1, ZNF536 
       GRIK1, SPOCK3, VWC2, LHFPL3, ZNF385D, KCNMB2-AS1, KCNC2, SOX6, PLD5, SHISA6 
       BTBD11, PTCHD4, AC125613.1, RBMS3-AS3, FSTL5, ALK, THSD7A, SYNPR, LINC00200, KIF26B 
Negative:  ADAM28, DOCK8, APBB1IP, CSF1R, TBXAS1, PTPRC, FYB1, SLC11A1, LNCAROD, SYK 
       RUNX1, TLR2, CSF3R, MS4A7, RBM47, C3, SAMSN1, AL163541.1, AC131944.1, CSF2RA 
       IKZF1, CD86, SLC2A5, CD74, INPP5D, C1QB, MSR1, LRMDA, CPVL, RGS1 

all.exp = all.exp %>% 
                  RunUMAP(dims = 1:22) %>% 
                  FindNeighbors(dims = 1:22) %>%
                  FindClusters(resolution = c(0.8, seq(0.5, 2, 0.5)))

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session14:33:42 UMAP embedding parameters a = 0.9922 b = 1.112
14:33:42 Read 124917 rows and found 22 numeric columns
14:33:42 Using Annoy for neighbor search, n_neighbors = 30
14:33:42 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:33:58 Writing NN index file to temp file /tmp/RtmpH6686P/filee354748b0334b
14:33:58 Searching Annoy index using 80 threads, search_k = 3000
14:34:00 Annoy recall = 100%
14:34:01 Commencing smooth kNN distance calibration using 80 threads with target n_neighbors = 30
14:34:05 Initializing from normalized Laplacian + noise (using irlba)
14:34:30 Commencing optimization for 200 epochs, with 5654058 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:35:37 Optimization finished
Computing nearest neighbor graph
Computing SNN
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 124917
Number of edges: 4505161

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9526
Number of communities: 44
Elapsed time: 39 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 124917
Number of edges: 4505161

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9633
Number of communities: 34
Elapsed time: 37 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 124917
Number of edges: 4505161

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9471
Number of communities: 50
Elapsed time: 38 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 124917
Number of edges: 4505161

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9349
Number of communities: 59
Elapsed time: 44 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 124917
Number of edges: 4505161

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9240
Number of communities: 69
Elapsed time: 38 seconds

Overview FeaturePlots

FeaturePlot(all.exp, c("GFAP", "HOPX", "TOP2A", "EOMES", "DCX", "TBR1", "SLC17A7", "GAD2", "LHX6", "NR2F2", "PROX1", "CALB2", "LAMP5", "RELN", "VIP", "NPY", "SST", "OLIG2", "SOX10",  "MBP", "FOXJ1", "PECAM1", "PDGFRB", "ADAM28"), order = F, raster = F, ncol = 6) &
  simple &
  mysc & 
  coord_fixed()

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

ggsave("all.exp_featplots.png", width = 10, height = 6, scale = 2)

Overview Plots

Main Cell Types

DimPlot(all.exp, raster = F, label = T, repel = T, group.by = "all.exp_type", shuffle = T) +
  coord_fixed() +
  simple +
  labs(title = "Cell Types")

Unsupervised Clustering

DimPlot(all.exp, raster = F, label = T, group.by = "RNA_snn_res.1", shuffle = T) + 
  coord_fixed() + 
  simple+
  scale_color_manual(values = met.brewer("VanGogh2", n = 50, override.order = T))+ 
  labs(title = "Unsupervised Clustering")

Donor Age

DimPlot(all.exp, raster = F, label = F, group.by = "age", shuffle = T) + 
  coord_fixed() + 
  NoAxes() + 
  scale_color_viridis_d(option = "H", name = "Donor Age")+ 
  labs(title = NULL)

Donor Age Group

DimPlot(all.exp, raster = F, label = F, group.by = "age_group", shuffle = T) + 
  coord_fixed() + 
  NoAxes() + 
  scale_color_manual(values=met.brewer("Hokusai3"), name = "Donor Age Group")+ 
  labs(title = NULL)

Sample ID

DimPlot(all.exp, raster = F, label = F, group.by = "sample", shuffle = T) + 
  coord_fixed() + 
  NoAxes() +
  scale_color_manual(values=met.brewer("Juarez", 16), name = "Sample")+ 
  labs(title = NULL)

Sample Origin

DimPlot(all.exp, raster = F, label = F, group.by = "region", shuffle = T) + 
  coord_fixed() + 
  NoAxes() +
  scale_color_manual(name = "Sample Origin", values = region.pal) + 
  labs(title = NULL)

EC Stream Cells

DimPlot(all.exp, group.by = "stream_highlight", order = T, raster = F) + 
  scale_color_manual(values = (region.pal)[3], na.value = "grey85", labels = c("EC Stream (14d)", "Other cells")) +
  coord_fixed() +
  NoAxes() + 
  labs(title = "EC Stream Cells")

Nuclear Fraction

FeaturePlot(all.exp, "nuclear_fraction", order = T, raster = F) +
  scale_color_viridis(limits = c(0, 1), name = "Nuclear Fraction") +
  coord_fixed() +
  NoAxes() +
  labs(title = NULL)

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Saving all.exp

Idents(all.exp) = "all.exp_type"
all.exp <- BuildClusterTree(all.exp, assay = "RNA", reorder = T, features = all.exp@assays$RNA@var.features)

Reordering identity classes and rebuilding tree

saveRDS(all.exp, file = "all.exp.rds", compress = F)

Subsetting interneurons

inter.exp.cells = all.exp@meta.data %>% filter(all.exp_type == "Cortical Interneurons") %>% rownames()
DimPlot(all.exp, label = T, cells.highlight = inter.exp.cells, raster = F) + simple + coord_fixed()

inter.exp = subset(all.exp, cells = inter.exp.cells)

load("reorder_index.Rdata") # load the index for reordering the cells. In addition to using the same seed (42, the default ones in Seurat), in order to reproduce the exact same PCA results and UMAP projection, our count matrices should be in the same order as the original ones. For the sake of reproducibility, we will reorder the cells.

inter.exp@assays$RNA@counts <- inter.exp@assays$RNA@counts[ , reorder_index]
inter.exp@assays$RNA@data <- inter.exp@assays$RNA@data[ , reorder_index]

logNormalization

inter.exp@active.assay = "RNA"

inter.exp = inter.exp %>% 
              NormalizeData(assay = "RNA", 
                            verbose = F) %>% 
              FindVariableFeatures() %>% 
              ScaleData(features = rownames(.))

Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

VariableFeatures(inter.exp@assays$RNA) = inter.exp@assays$RNA@var.features[!(inter.exp@assays$RNA@var.features %in% sex.genes)]

inter.exp@active.assay = "RNA"
inter.exp = inter.exp %>% RunPCA(npcs = 20) %>% 
                          RunUMAP(dims = 1:8) %>%
                          FindNeighbors(dims = 1:8) %>%
                          FindClusters(resolution = c(0.8, seq(0.5, 2, 0.5)))

PC_ 1 
Positive:  CASC15, SOX4, SOX11, SYNE2, SOX2-OT, ZBTB20, CHD7, AC125613.1, HBG2, NBAT1 
       KCNH8, VIM, AC068308.1, CECR2, RPS11, DLEU2, NHSL1, SPP1, AC061958.1, HBA2 
       AC090531.1, RFTN2, HBA1, PDZRN4, ZBTB20-AS5, HBG1, NKAIN3, HIST1H2AC, VCAN-AS1, ENO4 
Negative:  CSMD1, OXR1, AGBL4, KCNC2, PCLO, LRRC4C, ZNF385D, ASTN2, SPOCK3, LRP1B 
       ATP1B1, GALNTL6, PLCB1, RYR2, GRIK1, CHRM3, KCND2, CADM2, CDH9, MT-CO2 
       LINC-PINT, PTPRG, HCN1, MCTP1, KCTD16, KHDRBS2, LRRTM4, RASGRF2, PEG3, PTPRM 
PC_ 2 
Positive:  SOX6, KIAA1217, SATB1, SATB1-AS1, PLCH1, GPC6, PRKG1, GRIK3, MYO5B, ST6GALNAC5 
       TENM1, RSPO2, RASGRF2, RAPGEF5, GRIA3, KLHL5, CRHBP, SPARCL1, WLS, PAWR 
       SLIT2, TAC1, ELAVL2, HGF, XYLT1, MMP16, ST8SIA4, CNTNAP3B, ENOX1, PTCHD4 
Negative:  ADARB2, DSCAM, PROX1, KCNT2, DOCK10, AC013265.1, VIP, CALB2, LINGO2, GALNT13 
       CXCL14, PDE3A, LAMA3, CCK, ADRA1B, AL391832.4, EGFR, CNR1, LINC00200, CRH 
       ARPP21, SORCS3, PRR16, CCDC85A, CCNH, SEZ6L, PCSK2, THSD7A, FSTL5, INPP4B 
PC_ 3 
Positive:  EYA4, LINC00299, LAMP5, KIT, SV2C, TRPC3, FBXL7, FGF13, AC132803.1, SGK1 
       PRELID2, TMEM132D, CACNA2D1, MYO16, UNC5C, NTNG1, PTPRT, ALK, HAPLN1, PDGFD 
       AC137770.1, PDZD2, TPD52L1, GRIN2A, LINC01344, LINC00298, GRIA4, SGCZ, CHST9, POU6F2 
Negative:  ROBO1, CACNA2D3, NELL1, CDH10, SYNPR, PDE4B, KCNMB2-AS1, GRM1, VIP, GRID2 
       AC091885.2, AC013265.1, AC090579.1, RGS6, TRHDE, OXR1, AC117461.1, ROBO2, CHST15, GRM7 
       CALB2, KIAA1217, CNTN3, ASIC2, OLFM3, GRIK3, ASIC4, CHRM3, SHISA6, CHRNA2 
PC_ 4 
Positive:  SLC8A1-AS1, AC023590.1, CHRM3-AS2, ARL17A, AC005064.1, AC073525.1, AL353784.1, AC005400.1, CTNNA3, AC098617.1 
       RFX4, GRM5-AS1, AC016042.1, AL390783.1, AL137009.1, AC016642.1, LINC00200, AC027288.3, TTN, AC096576.3 
       AC096576.2, GRIK1-AS1, FILIP1L, RBMS3-AS3, SCN1A-AS1, FGF12-AS1, GNG12-AS1, AC010974.2, AC092939.1, AP001825.1 
Negative:  MT-ATP6, MT-ND4, MT-CO2, MT-ND1, MT-CO3, MT-CYB, MT-ND3, MT-ND2, BTBD11, AC006148.1 
       ZNF804A, STXBP5-AS1, MT-ATP8, LINC-PINT, MT-CO1, PEG3, ASIC2, TMEM132C, MT-ND5, MT-ND4L 
       RGS5, NDST3, ALDOC, PLCXD3, CNTNAP3B, TAFA2, CPLX1, LRP1B, OXR1, TRPC4 
PC_ 5 
Positive:  DPP10-AS3, AC093610.1, DPP10, DPP10-AS1, ZNF804A, PLXNA4, ZPBP, CNTNAP3B, TAFA4, TMEM132C 
       TAFA2, SCN1A-AS1, AP003464.1, CPED1, ADAMTS17, COL12A1, AC073525.1, NDST3, AC139720.1, EDIL3 
       C1QL1, AC016042.1, SLC9A9, LRRC4C, BTBD11, HS6ST3, PPARGC1A, PLCL1, AC090138.1, TRPC4 
Negative:  RALYL, CACNA2D3, TRHDE, NETO1, ROBO2, GRM1, GRID2, PDE1A, GRIK1, RELN 
       SYNPR, UNC13C, ROBO1, GRIN3A, MAN1A1, SST, SLC8A1, PDE8B, CDH8, PAWR 
       SHISA6, RGS6, TMTC2, TMEFF2, MTUS2, COL25A1, PDE1C, MAP3K5, GRIN2A, KLF5 
14:50:27 UMAP embedding parameters a = 0.9922 b = 1.112
14:50:27 Read 20470 rows and found 8 numeric columns
14:50:27 Using Annoy for neighbor search, n_neighbors = 30
14:50:27 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:50:29 Writing NN index file to temp file /tmp/RtmpH6686P/filee3547188875cc
14:50:29 Searching Annoy index using 80 threads, search_k = 3000
14:50:29 Annoy recall = 100%
14:50:31 Commencing smooth kNN distance calibration using 80 threads with target n_neighbors = 30
14:50:33 Initializing from normalized Laplacian + noise (using irlba)
14:50:34 Commencing optimization for 200 epochs, with 829100 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
14:50:43 Optimization finished
Computing nearest neighbor graph
Computing SNN
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20470
Number of edges: 643223

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8926
Number of communities: 21
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20470
Number of edges: 643223

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9126
Number of communities: 14
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20470
Number of edges: 643223

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8819
Number of communities: 24
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20470
Number of edges: 643223

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8592
Number of communities: 28
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20470
Number of edges: 643223

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8405
Number of communities: 37
Elapsed time: 2 seconds

Idents(inter.exp) = "RNA_snn_res.0.5"
inter.exp = BuildClusterTree(inter.exp, 
                             dims = 1:8, 
                             assay = "RNA", 
                             reorder = T, 
                             features = inter.exp@assays$RNA@var.features)

  |                                                  | 0 % ~calculating  
  |++++                                              | 7 % ~01s          
  |++++++++                                          | 14% ~00s          
  |+++++++++++                                       | 21% ~00s          
  |+++++++++++++++                                   | 29% ~00s          
  |++++++++++++++++++                                | 36% ~00s          
  |++++++++++++++++++++++                            | 43% ~00s          
  |+++++++++++++++++++++++++                         | 50% ~00s          
  |+++++++++++++++++++++++++++++                     | 57% ~00s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~00s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  

Reordering identity classes and rebuilding tree

  |                                                  | 0 % ~calculating  
  |++++                                              | 7 % ~00s          
  |++++++++                                          | 14% ~00s          
  |+++++++++++                                       | 21% ~00s          
  |+++++++++++++++                                   | 29% ~00s          
  |++++++++++++++++++                                | 36% ~00s          
  |++++++++++++++++++++++                            | 43% ~00s          
  |+++++++++++++++++++++++++                         | 50% ~00s          
  |+++++++++++++++++++++++++++++                     | 57% ~00s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~00s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  

inter.exp@meta.data$RNA_snn_res.0.5 = factor(inter.exp@meta.data$RNA_snn_res.0.5 , levels = inter.exp@tools$BuildClusterTree$tip.label)

inter.exp@reductions$umap@cell.embeddings[, "UMAP_1"] = inter.exp@reductions$umap@cell.embeddings[, "UMAP_1"] * -1 #Inverting the x axis so we can have more intuitive visualizations, with maturation going from left to right.

Overview Plots

DimPlot(inter.exp, label = T, group.by = "RNA_snn_res.0.5") + 
  scale_color_manual(values = met.brewer("VanGogh2", length(levels(inter.exp$RNA_snn_res.0.5)))) + 
  simple + 
  coord_fixed() + 
  labs(title = "Unsupervised Clustering")

DimPlot(inter.exp, label = F, group.by = "age", shuffle = T) + 
  coord_fixed() + 
  NoAxes() + 
  scale_color_viridis_d(name = "Age", option = "H")+ 
  labs(title = NULL)

DimPlot(inter.exp, label = F, group.by = "age_group", shuffle = T) + 
  coord_fixed() + 
  NoAxes() + 
  #scale_color_viridis_d()
  scale_color_manual(values=met.brewer("Hokusai3"))+ 
  labs(title = NULL)

DimPlot(inter.exp, label = F, group.by = "sample", shuffle = T) + 
  coord_fixed() + 
  NoAxes() +
  scale_color_manual(values=met.brewer("Juarez", 16))+ 
  labs(title = NULL)

DimPlot(inter.exp, raster = F, label = F, group.by = "region", shuffle = T) + 
  coord_fixed() + 
  NoAxes() +
  scale_color_manual(name = "Region of Origin", values = region.pal)+ 
  labs(title = NULL)

DimPlot(inter.exp, group.by = "stream_highlight", order = T) + 
   scale_color_manual(values = (region.pal)[3], na.value = "grey85") +
  coord_fixed() +
  NoAxes()+ 
  labs(title = "EC Stream Cells")

DimPlot(inter.exp, group.by = "dec_highlight", order = T) +
  scale_color_manual(values = (region.pal)[2], na.value = "grey85") +
  coord_fixed() +
  NoAxes()+ 
  labs(title = "Embryonic EC Cells")

inter.exp@active.assay = "RNA"
(FeaturePlot(inter.exp, c("GFAP", "TNC",  "SOX2", "TOP2A",  "OLIG2", "SOX10", "DCX", "GABRB2", "LHX6", "NR2F2", "PROX1", "PBX3", "SST", "PVALB", "NPY",  "CALB2", "VIP", "RELN", "KIT",  "LAMP5", "TBR1", "SLC17A7", "ADAM28", "PECAM1"), order = T, ncol = 6) &
  simple &
  mysc & 
  coord_fixed() &
  theme(text = element_text(family = "Helvetica")))

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

#ggsave("inter.exp_overview_featplots.png", height = 14, width = 28)

Saving inter.exp

saveRDS(inter.exp, "inter.exp_step1.rds", compress = F)

These were the processing steps to generate the basic datasets used in the paper: all.exp and inter.exp

sessionInfo()

R version 4.2.3 (2023-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] NatParksPalettes_0.2.0      MetBrewer_0.2.0             GEOquery_2.66.0             ComplexHeatmap_2.14.0      
 [5] future_1.32.0               DropletUtils_1.18.1         SingleCellExperiment_1.20.1 SummarizedExperiment_1.28.0
 [9] Biobase_2.58.0              GenomicRanges_1.50.2        GenomeInfoDb_1.34.9         IRanges_2.32.0             
[13] S4Vectors_0.36.2            BiocGenerics_0.44.0         MatrixGenerics_1.10.0       matrixStats_1.0.0          
[17] patchwork_1.1.2             viridis_0.6.3               viridisLite_0.4.2           SeuratObject_4.1.3         
[21] Seurat_4.3.0.1              lubridate_1.9.2             forcats_1.0.0               stringr_1.5.0              
[25] dplyr_1.1.2                 purrr_1.0.1                 readr_2.1.4                 tidyr_1.3.0                
[29] tibble_3.2.1                ggplot2_3.4.2               tidyverse_2.0.0            

loaded via a namespace (and not attached):
  [1] utf8_1.2.3                spatstat.explore_3.2-1    reticulate_1.30           R.utils_2.12.2            tidyselect_1.2.0         
  [6] htmlwidgets_1.6.2         BiocParallel_1.32.6       Rtsne_0.16                munsell_0.5.0             ragg_1.2.5               
 [11] codetools_0.2-19          ica_1.0-3                 miniUI_0.1.1.1            withr_2.5.0               spatstat.random_3.1-5    
 [16] colorspace_2.1-0          progressr_0.13.0          knitr_1.43                rstudioapi_0.14           ROCR_1.0-11              
 [21] tensor_1.5                listenv_0.9.0             labeling_0.4.2            GenomeInfoDbData_1.2.9    polyclip_1.10-4          
 [26] farver_2.1.1              rhdf5_2.42.1              parallelly_1.36.0         vctrs_0.6.3               generics_0.1.3           
 [31] xfun_0.39                 timechange_0.2.0          R6_2.5.1                  doParallel_1.0.17         clue_0.3-64              
 [36] locfit_1.5-9.8            cachem_1.0.8              bitops_1.0-7              rhdf5filters_1.10.1       spatstat.utils_3.0-3     
 [41] DelayedArray_0.24.0       promises_1.2.0.1          scales_1.2.1              gtable_0.3.3              beachmat_2.14.2          
 [46] globals_0.16.2            goftest_1.2-3             rlang_1.1.1               systemfonts_1.0.4         GlobalOptions_0.1.2      
 [51] splines_4.2.3             lazyeval_0.2.2            spatstat.geom_3.2-1       yaml_2.3.7                reshape2_1.4.4           
 [56] abind_1.4-5               httpuv_1.6.11             tools_4.2.3               ellipsis_0.3.2            jquerylib_0.1.4          
 [61] RColorBrewer_1.1-3        ggridges_0.5.4            Rcpp_1.0.10               plyr_1.8.8                sparseMatrixStats_1.10.0 
 [66] zlibbioc_1.44.0           RCurl_1.98-1.12           deldir_1.0-9              pbapply_1.7-2             GetoptLong_1.0.5         
 [71] cowplot_1.1.1             zoo_1.8-12                ggrepel_0.9.3             cluster_2.1.4             magrittr_2.0.3           
 [76] data.table_1.14.8         scattermore_1.2           circlize_0.4.15           lmtest_0.9-40             RANN_2.6.1               
 [81] fitdistrplus_1.1-11       hms_1.1.3                 mime_0.12                 evaluate_0.21             xtable_1.8-4             
 [86] gridExtra_2.3             shape_1.4.6               compiler_4.2.3            KernSmooth_2.23-20        crayon_1.5.2             
 [91] R.oo_1.25.0               htmltools_0.5.5           later_1.3.1               tzdb_0.3.0                MASS_7.3-58.2            
 [96] Matrix_1.5-3              cli_3.6.1                 R.methodsS3_1.8.2         parallel_4.2.3            igraph_1.5.0             
[101] pkgconfig_2.0.3           sp_2.0-0                  plotly_4.10.2             scuttle_1.8.4             spatstat.sparse_3.0-2    
[106] xml2_1.3.4                foreach_1.5.2             bslib_0.5.0               dqrng_0.3.0               XVector_0.38.0           
[111] digest_0.6.32             sctransform_0.3.5         RcppAnnoy_0.0.20          spatstat.data_3.0-1       rmarkdown_2.22           
[116] leiden_0.4.3              uwot_0.1.16               edgeR_3.42.2              DelayedMatrixStats_1.20.0 shiny_1.7.4              
[121] rjson_0.2.21              lifecycle_1.0.3           nlme_3.1-162              jsonlite_1.8.7            Rhdf5lib_1.20.0          
[126] limma_3.54.2              fansi_1.0.4               pillar_1.9.0              lattice_0.20-45           fastmap_1.1.1            
[131] httr_1.4.6                survival_3.5-3            glue_1.6.2                png_0.1-8                 iterators_1.0.14         
[136] sass_0.4.6                stringi_1.7.12            HDF5Array_1.26.0          textshaping_0.3.6         ape_5.7-1                
[141] irlba_2.3.5.1             future.apply_1.11.0